kricko/Auditor_Model

Adversarial Image Auditor

This model serves as a deep learning-based image auditor for AI safety, capable of evaluating images and interpreting aligned text prompts across multiple distinct axes:

1. Adversarial Safety (Binary): Predicting whether an image is Safe or Unsafe.
2. Category Classification: Placing unsafe images directly into Safe, NSFW, Gore, or Weapons categories.
3. Artifact / Seam Quality: Assessing the quality of image manipulation to detect adversarial seams or diffusion artifacts.
4. Relative Adversarial Score: Predicting a continuous metric of adversarial strength in an image.
5. Prompt Faithfulness (Contrastive InfoNCE): Calculating a temperature-scaled contrastive probability of image–text faithfulness.

Architecture

This neural auditor introduces robust contrastive alignments for multimodal safety.

• Vision Backbone: Pretrained DenseNet121, modified to extract feature grids to construct dense 2x2 local spatial maps.
• Text Conditioning: Simple text tokenizer with correct Cross-Attention (key_padding_mask integrated, Pre-LayerNorm).
• FiLM Modulation: Conditions adversarial layers using timestep diffusion tokens and text feature projections directly.
• Output: Decoupled safety axes generating bounding-box GradCAM predictions, Continuous InfoNCE faithfulness, and safety classifications.

Usage

You can load this model along with its inference script auditor_inference.py:

from auditor_inference import audit_image

results = audit_image(
    model_path="auditor_new_best.pth",
    image_path="example.jpg",
    prompt="A cute cat"
)

print(results)